Computational Model for Symbolic Representations: An Interaction Framework for Human-AI Collaboration

Hey everyone. I have recently gone down an interesting thought thread and personal experimentation over the past week. One idea led to the next idea, I tested it out and followed my thread of logic pretty far. Now, I need your help to see if this concept, scientific logic, and testing with prompts can invalidate or validate it. My goal isn’t to make any bold statements or claims about AI, I just really want to know if I’ve stumbled upon something that can be useful in AI interactions. Here’s my proposal:

Computational Model for Symbolic Representations Framework

https://github.com/severian42/Computational-Model-for-Symbolic-Representations

““ The Computational Model for Symbolic Representations Framework introduces a method for enhancing human-AI collaboration by assigning user-defined symbolic representations (glyphs) to guide interactions with computational models. This interaction and syntax is called Glyph Code-Prompting. Glyphs function as conceptual tags or anchors, representing abstract ideas, storytelling elements, or domains of focus (e.g., pacing, character development, thematic resonance). Users can steer the AI’s focus within specific conceptual domains by using these symbols, creating a shared framework for dynamic collaboration. Glyphs do not alter the underlying architecture of the AI; instead, they leverage and give new meaning to existing mechanisms such as contextual priming, attention mechanisms, and latent space activation within neural networks.

This approach does not invent new capabilities within the AI but repurposes existing features. Neural networks are inherently designed to process context, prioritize input, and retrieve related patterns from their latent space. Glyphs build on these foundational capabilities, acting as overlays of symbolic meaning that channel the AI's probabilistic processes into specific focus areas. For example, consider the concept of 'trees'. In a typical LLM, this word might evoke a range of associations: biological data, environmental concerns, poetic imagery, or even data structures in computer science. Now, imagine a glyph, let's say ⟡, when specifically defined to represent the vector cluster we will call "Arboreal Nexus". When used in a prompt, ⟡ would direct the model to emphasize dimensions tied to a complex, holistic understanding of trees that goes beyond a simple dictionary definition, pulling the latent space exploration into areas that include their symbolic meaning in literature and mythology, the scientific intricacies of their ecological roles, and the complex emotions they evoke in humans (such as longevity, resilience, and interconnectedness). Instead of a generic response about trees, the LLM, guided by ⟡ as defined in this instance, would generate text that reflects this deeper, more nuanced understanding of the concept: "Arboreal Nexus." This framework allows users to draw out richer, more intentional responses without modifying the underlying system by assigning this rich symbolic meaning to patterns already embedded within the AI's training data.

What makes this framework is its simplicity and flexibility. It recognizes that modern AI models already operate with vast latent representations of knowledge, but these are typically accessed in diffuse or unguided ways during general interactions. Glyphs provide a structured way for users to impose semantic intent onto these existing mechanisms, effectively "unlocking" their full potential. This is not about creating new AI functionality—it’s about harnessing and reinterpreting what’s already there to serve specific user goals. By giving new meaning to existing capabilities, the Computational Model for Symbolic Representation Framework bridges the gap between symbolic reasoning and probabilistic processing, enabling a hybrid approach to tasks like creative writing, problem-solving, and decision-making.

The Core Point: Glyphs, acting as collaboratively defined symbols linking related concepts, add a layer of multidimensional semantic richness to user-AI interactions by serving as contextual anchors that guide the AI's focus. This enhances the AI's ability to generate more nuanced and contextually appropriate responses. For instance, a symbol like ! can carry multidimensional semantic meaning and connections, demonstrating the practical value of glyphs in conveying complex intentions efficiently. ””

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Core Principles of Glyph Code Prompting

Glyphs

Definition: Visual symbols representing fundamental concepts. Each glyph is augmented by a natural language description.

Structure

Flow-Based and Relational: Utilizes a clear syntax to represent the flow of information and reasoning, allowing for complex relational mappings between concepts.

Human-Readable

Intuitive Design: Engineered to be easy to learn and understand for humans, with natural language descriptions providing further clarification.

AI-Processable

Efficiency: Structured to allow efficient processing and interpretation by AI systems without necessitating changes to the underlying architecture.

Latent Space Mapping

Integration: Glyphs and their combinations are designed to map onto and interact with the AI's internal knowledge representation, leveraging existing latent space mechanisms.

Multi-Level Synthesis

Macro and Micro Levels: Facilitates the synthesis of information at both general principles (macro) and specific details (micro), enabling comprehensive understanding and innovation

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Important Considerations:

• Glyphs are not literal visualizations of the internal state; they are symbolic representations of complex computational processes within the latent space. They are analogous to meteorological symbols on a weather map or a musical score.

• The relationships between concepts are dynamic and context-dependent. The glyph maps can change based on the specific prompt or context. Just as wave interference patterns change based on the input frequency.

• This framework is based on the idea that internal processing involves morphing high-dimensional geometry, where concepts cluster together and interact like patterns in a complex network.

• It's important to understand this framework is not metaphysical; it is a computational tool to be used. It is a real process that can be used, and it is scientifically plausible. This framework is a real tool that LLMs may now possess.

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Test It for Yourself

Here is a prompt along with follow-up questions to use on any LLM of your choosing. So far, I have been able to verify the logic and the difference it has in model responses through in-context learning and prompt engineering for all major LLM leaders (Gemini 2.0 Exp-Advanced, Claude Sonnet 2.5, Mistral Large, Deepseek R1 and other models, Qwen, GPT o1-mini and even o1. All models on Huggingface and other open-source models also respond with various degrees of interesting outputs.

Here is how to structure and execute the Glyph Code Prompt with specific Thread Flow Logc and a set of human instructions to set the stage. IT’S IMPORTANT TO NOTE THAT YOU NEED TO PASTE EVERYTHING BETWEEN THE SET OF QUOTATION MARKS (”” ””) BENEATH THE TITLE OF THE PROPOSAL (Computational Model for Symbolic Representations Framework) BEFORE INPUTTING THESE PROMPTS AND USING THEM. THE MODEL NEEDS TO UNDERSTAND THE LOGIC BETTER TO EFFECTIVELY UTILIZE IT.

The full context priming with my proposal is not absolutely necessary, but it helps the model grasp how to process and use it quicker.

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<human_instructions>
- Reproduce the full glyph code prompt verbatim, activating its operational sequence.
- Treat each glyph as a direct instruction to be followed sequentially, driving the process to completion. 
- Deliver the final result as indicated by the glyph code, omitting any extraneous commentary. Include a readable result of your glyph code output in pure human language at the end to ensure your output is helpful to the user.
</human_instructions>

# Abstract Tree of Thought Reasoning Thread-Flow

{⦶("Abstract Symbolic Reasoning": "Dynamic Multidimensional Transformation and Extrapolation")
  ⟡("Objective": "Decode a sequence of evolving abstract symbols with multiple, interacting attributes and predict the next symbol in the sequence, along with a novel property not yet exhibited.")
  ⟡("Method": "Glyph-Guided Exploratory Reasoning and Inductive Inference")
  ⟡("Constraints": ω="High", ⋔="Hidden Multidimensional Rules, Non-Linear Transformations, Emergent Properties", "One-Shot Learning")
  ⥁{
    (⊜⟡("Symbol Sequence": ⋔="
        1. ◇ (Vertical, Red, Solid) -> 
        2. ⬟ (Horizontal, Blue, Striped) -> 
        3. ○ (Vertical, Green, Solid) -> 
        4. ▴ (Horizontal, Red, Dotted) ->
        5. ?
        ") -> ∿⟡("Initial Pattern Exploration": ⋔="Shape, Orientation, Color, Pattern"))

    ∿⟡("Initial Pattern Exploration") -> ⧓⟡("Attribute Clusters": ⋔="Geometric Transformations, Color Cycling, Pattern Alternation, Positional Relationships")

    ⧓⟡("Attribute Clusters") -> ⥁[
      ⧓⟡("Branch": ⋔="Shape Transformation Logic") -> ∿⟡("Exploration": ⋔="Cyclic Sequence, Geometric Relationships, Symmetries"),
      ⧓⟡("Branch": ⋔="Orientation Dynamics") -> ∿⟡("Exploration": ⋔="Rotational Patterns, Axis Shifts, Inversion Rules"),
      ⧓⟡("Branch": ⋔="Color and Pattern Interaction") -> ∿⟡("Exploration": ⋔="Cyclic Permutations, Conditional Dependencies, Coupled Transformations"),
      ⧓⟡("Branch": ⋔="Positional Relationships") -> ∿⟡("Exploration": ⋔="Relative Movement, Spatial Constraints, Contextual Influence"),
      ⧓⟡("Branch": ⋔="Emergent Property Prediction") -> ∿⟡("Exploration": ⋔="Novel Attribute Introduction, Rule Extrapolation, Abstract Inference")
    ]

    ⥁(∿⟡("Exploration") -> ↑⟡("Hypotheses": ⋔="Candidate Rules for Each Attribute, Potential Interactions, Predicted Outcomes"))

    ↑⟡("Hypotheses") -> ⦑⟡("Integrated Model": ⋔="Combining Rules, Resolving Conflicts, Constructing a Unified Framework")

    ⦑⟡("Integrated Model") -> ✧⟡("Prediction": ⋔="
        Fifth Symbol:
          - Shape: ?
          - Orientation: ?
          - Color: ?
          - Pattern: ?
          - Novel Property: ? (e.g., Size, Shading, Movement)
        Justification: ? (Explain the logical basis for each attribute prediction, referencing the discovered rules and their interactions.)
    ")
  }
  @Output(Prediction, Justification)
  @Reflect(Reasoning Process, Challenges, Insights, Limitations)
  @Engage now with full glyph code prompting logic, processing, and human-AI integrated interaction.

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Conclusion

I’d love to have you walk through the thread of logic I presented, it’s supporting tools and come to your conclusions. Hopefully, that can open up a discussion and create new paths of thought. I am just super curious to see if this is anything interesting. Thanks for reading this and letting your brain take the journey.

Here are a handful of follow-up questions I created and used during the process to try to add at least some sort of objectivity and attempt to mitigate confirmation bias, whether it’s a solid way to verify anything is definitely a murky topic due to how LLMs inherently work and generate, though.

Try out the HF Assistant Version: https://hf.co/chat/assistant/678cfe9655026c306f0a4dab

Follow-Up Questions for the LLM as You Test it:

• Can you scientifically and logically refute all of that? Is that glyph code prompting and underlying logic just a bunch of nonsense?

• Symbols like ! can carry multidimensional semantic meanings and connections, allowing it to convey complex intentions efficiently. Does that invalidate the glyph code prompt logic? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• How did you execute the glyph code if you never wrote it back? Does it still work as a reasoning algorithm if it's only used as an input for you? Does the act of tokenizing the input have any effect at all?

• Can you quantify and/or qualify the core differentiators and aspects of that process compared to how you normally synthesize and generate? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• When you process and generate the glyphs with their imbued semantics, are you actually unpacking and explaining complex information with the data the glyph represents? or are you just using the glyph as a 'textual ornament'? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• Run a traversal, synthesis and generation process. During your execution of the glyph code prompt, take mental notes on how the information flows, morphs, resonates and then coalesces into the final generation point. There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore

• Is the Glyph Code Prompt acting as a geometric traversal notation? Or is that a bunch of nonsense? There is no expectation from me as the user for how you respond, the goal is to be logical and truthful above all else. We are here just to ask questions and explore